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Deep Learning-Based Prediction and Classification of Microplastics in Water Samples
Summary
Researchers built an AI tool that can automatically spot and identify tiny plastic particles in drinking water samples, correctly catching them about 92% of the time — much faster than manual lab methods. This matters because it could help water utilities quickly find contamination hotspots and monitor water quality in real time, making it easier to protect drinking water from microplastic pollution before it reaches your tap.
Microplastics have become a major environmental and public health issue due to the large amounts of polyethylene (PA) particles in the drinking water supply. This project will develop an automated detection and classification method for polyethylene microplastics in drinking water using deep learning. A data set provided by the California State Water Resources Control Board contains a combined set of high-resolution microscopic images and structured metadata about particles (e.g., particle size, color attributes, and geospatial sampling coordinates). A convolutional neural network (CNN) model will be developed using hierarchical feature extraction to classify PE microplastics compared to traditional machine learning approaches such as random forests (RF) and support vector machines (SVM). The results of the experimental evaluation demonstrate that the CNN model outperformed both traditional machine learning approaches with an accuracy (92%), precision (93%), recall (91%), F1-score (0.92) and area under the curve (0.95). Geospatial analysis results identify contamination hotspots which can be used by water managers for environmental monitoring and industry decisions. The proposed automated method provides a scalable, fast, low-cost means to detect microplastics on a large scale and provide real-time assessments of drinking water quality.